Performance analysis for lattice-based speech indexing approaches using words and subword units
IEEE Transactions on Audio, Speech, and Language Processing
Query-driven strategy for on-the-fly term spotting in spontaneous speech
EURASIP Journal on Audio, Speech, and Music Processing - Special issue on scalable audio-content analysis
Direct posterior confidence for out-of-vocabulary spoken term detection
ACM Transactions on Information Systems (TOIS)
Spoken Content Retrieval: A Survey of Techniques and Technologies
Foundations and Trends in Information Retrieval
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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We present several novel approaches to the Out of Vocabulary (OOV) query problem for spoken audio: indexing based on syllable-like units called particles and query expansion according to acoustic confusability for a word index. We also examine linear and OOV-based combination of indexing schemes. We experiment on 75 h of broadcast news, comparing our techniques to a word index, a phoneme index and a phoneme index queried with phoneme sequences. Our results show that our approaches are superior to both a word index and a phoneme index for OOV words, and have comparable performance to the sequence of phonemes scheme. The particle system has worse performance than the acoustic query expansion scheme. The best system uses word queries for in-vocabulary words and a linear combination of the phoneme sequence scheme and acoustic query expansion for OOV words. Using the best possible weights for linear combination, this system improves the average precision from 0.35 for a word index to 0.40, a result only obtainable if the weights could be learnt on a development query set. The next best system used a word index for in-vocabulary words and the phoneme sequence system otherwise and had average precision of 0.39.